Details

Autor(en) / Beteiligte

• Baer, Manuel F.
• Wartmann, Flurina
• Fagerholm, Nora
• Purves, Ross S.

Titel

Extracting sensory experiences and cultural ecosystem services from actively crowdsourced descriptions of everyday lived landscapes

Ist Teil von

• Ecosystems and people (Abingdon, England), 2024-12, Vol.20 (1)

Ort / Verlag

Taylor & Francis Group

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• ABSTRACTWhat cultural ecosystem services (CES) do people perceive in their immediate surroundings, and what sensory experiences are linked to these ecosystem services? And how are these CES and experiences expressed in natural language? In this study, we used data generated through a gamified application called Window Expeditions, where people uploaded short descriptions of landscapes they were able to experience through their windows during the COVID-19 pandemic. We used a combination of annotation, close reading and distant reading using natural language processing and graph analysis to extract CES and sensory experiences and link these to biophysical landscape elements. In total, 272 users contributed 373 descriptions in English across more than 40 countries. Of the cultural ecosystem services, recreation was the most prominently described, followed by heritage, identity and tranquility. Descriptions of sensory experiences focused on the visual but also included auditory experiences and touch and feel. Sensory experiences and cultural ecosystem services varied according to biophysical landscape elements, with, for example, animals being more associated with sound and touch/feel and heritage being more associated with moving objects and the built environment. Sentiments also varied across the senses, with the visual being more strongly associated with positive experiences than other senses. This study showed how a hybrid approach combining manual analysis and natural language processing can be productively applied to landscape descriptions generated by members of the public, and how CES on everyday lived landscapes can be extracted from such data sources.